How to filter men and women on WhatsApp? Can male and female users be separated?

DoWhen screening WhatsApp customers, some people would like to separate male users from female users to facilitate subsequent use of different products, content, or activities. The requirement itself is not complicated, but the places where problems are most likely to go wrong are also clear:A WhatsApp number itself cannot reliably tell you whether the user is male or female.

If there is a gender field in the customer profile, for example, the user fills it out when registering, has saved it in the member profile, actively selected it when registering for an event, or has clearly confirmed it in customer service communication, then the existing gender information can be combined withWhatsApp number status is combined and continues to be grouped into men and women.

If you only have a batch of mobile phone numbers and no other information, it is not suitable to guess gender through avatars, nicknames or numbers.

When filtering WhatsApp for men and women, you really need to check whether there is a gender field first.

Suppose there is a batch of50,000 pieces of overseas customer data.

It contains:

Phone number

nation

Name

gender

Customer source

product interest

This kind of data is more suitable for continued screening of men and women.

First sort out the male users separately, then sort out the female users separately, and then continue to check which numbers have been activatedWhatsApp.

Finally you can get:

Male＋WhatsApp has been activated

Female +WhatsApp has been activated

Gender unknown +WhatsApp has been activated

This division is very clear.

Gender comes from the company’s existing customer information.WhatsApp status comes from number screening, and the two types of information are used in combination.

Only a mobile phone number cannot be used to directly determine the gender.

If the original data only has phone numbers, don't putWhatsApp male and female filtering is understood to directly return male or female after entering the number.

Mobile phone numbers do not contain reliable gender information.

WhatsApp accounts will not automatically disclose the real user’s gender just because of the number itself.

Some people may want to judge by avatar, but this method has many problems.

Users may not use real-life avatars;

May use companyLogo;

May use pictures of pets, landscapes, or cartoons;

You may also use an avatar that has nothing to do with you.

The same goes for nicknames.

Many overseas users use English names, online names, company names or abbreviations. It is easy to make mistakes in determining gender based on names alone.

Therefore, when you really need to classify male and female users, it is best to start with existing and clear customer information.

Which gender data is more suitable for screening?

There are several common sources.

The first is for users to fill it out when registering.

For example, in the official website registration, membership system, event registration or product trial forms, there are already gender options.

The second type is the company’s ownCRM customer data.

If sales and customer service have previously confirmed the user information in the normal course of business, they can be used directly.

The third type is membership or historical order data.

Some businesses themselves will save the basic information provided by customers, and these fields can continue to be used when subsequent customers sort it out.

The fourth type is information actively supplemented by users.

For example, during customer service communication or account information update, the user completes the gender information himself.

This data is much more reliable than guessing based on a number, profile picture, or nickname.

Digital Planet is better suited to solveWhatsApp status layer

If the company already has a batch of customer numbers that contain gender fields, you can sort out the numbers themselves first.

For example, unify the international number format, remove duplicate numbers, and then filter through Digital PlanetWhatsApp activation status.

For example, the original data is:

American male customer

French female client

Thai male customer

British female customer

After number sorting andAfter WhatsApp filters, you can continue to get:

America＋Male＋WhatsApp has been activated

France＋Female＋WhatsApp has been activated

Thailand＋Male＋WhatsApp has been activated

UK+Female+WhatsApp has been activated

In this way, subsequent marketing can continue to be segmented according to business needs.

Digital Planet is responsible for existing numbers andWhatsApp platform status, male or female labels should still come from the company’s original real customer data.

After men and women are separated, it does not mean that they must do two completely different sets of marketing.

This is another place where it’s easy to get sidetracked.

As soon as some people see the two labels of male and female, they feel that they must prepare two sets of advertisements and two sets of words respectively.

Actually, it depends on the product itself.

If you sell products with obvious audience differences, such as some clothing, beauty, maternal and child care, men's care, etc., gender grouping may be more meaningful.

But if you are selling enterprise software, industrial equipment, logistics services,For B2B tools, gender is usually not the most important criteria.

In this case, what is more worthy of watching than men and women may be:

industry

Company size

Position

product requirements

nation

Latest inquiry

soWhatsApp male and female filtering is only an optional label and is not required for all businesses.

The truly practical approach is to look at gender and product interests together.

Looking at men or women alone, the range is still too large.

For example there areThere are 10,000 female WhatsApp users, but some of them follow product A, some follow product B, and some just sign up for historical events.

If you put them all on the same list, sales will still be chaotic.

More practical is to continue the combination.

for example:

Female +WhatsApp has been activated + follow beauty products

Male＋WhatsApp has been activated + follow outdoor products

Female +WhatsApp has been activated + recent inquiries

Male＋WhatsApp has been activated + historical customer

In this way, gender labels can truly participate in customer stratification.

Otherwise, just splitting one large table into two large tables has limited meaning.

Do not forcibly add labels to data with unknown gender

There are often some genders in the customer database that are empty.

This kind of data can be kept as"unknown".

Don’t just guess a result by name, avatar, or algorithm just to make the table look complete.

Because if you guess wrong, subsequent customer communications will be even more awkward.

A more practical way to divide it is into three groups:

male

female

unknown

If users add their own information in the future, just update it.

Customer data is not permanently unchanged after being sorted out once, but can be gradually improved along with normal business information.

Which scenarios are suitable for WhatsApp male and female screening?

If the business itself has obvious audience differences, male and female screening will be more valuable.

For example, clothing promotion.

Men and women focus on different styles, so the content can be separated.

Such as beauty and care products.

The audience for some products is relatively clear, so irrelevant content can be reduced.

Another example is event marketing.

If certain activities themselves have clear user positioning, the target group can also be screened based on existing gender information.

But if there are no obvious gender differences in the product, there is no need to screen for the sake of screening.

Customer needs are often more important than gender.

The source of customers must also be retained together

Even if the male and female groups have been completed, do not delete the original source of customers.

A female user actively inquired from the official website.

Another female user attended the event just two years ago.

Although two people have the same gender,WhatsApp has also been launched, but the business value is completely different.

So the final customer table is best kept at the same time:

nation

Phone number

gender

WhatsApp status

Customer source

Follow products

most recent interaction

Current follow-up status

In this way, after sales get the list, they can not only filter by men and women, but also continue to sort by demand and time.

Don’t turn gender screening into stereotypes

Gender grouping is just to reduce irrelevant contacts when there is a real business need.

Just because the customer is male does not mean that he will like a certain type of product by default.

Nor can we directly judge consumption habits or purchasing ability just because the customer is female.

Whether you are really interested or not depends on the product selection, consultation content and follow-up behavior left by the customer.

So even if you doAfter WhatsApp’s male and female screening, real needs should continue to determine marketing content.

A relatively simple screening process

If there is already a gender field in the customer data, it can be processed like this.

First sort out existing customer information.

Unified international number format.

Remove duplicate numbers.

Keep the original male, female, and unknown tags.

Then filter through the digital planetWhatsApp activation status.

Finally, gender, platform status, product interest, and recent business behavior were recombined.

What you get in this way is not a simple list of male and female numbers, but a piece of customer data that can continue to be used for sales and operations.

Screening men and women on WhatsApp is not difficult in itself. What is difficult is that the data source must be reliable.Gender should be filled in by the user himself or from existing business information.WhatsApp detection is responsible for confirming the number's platform status. The combination of the two can be used to group customers, but do not use mobile phone numbers, avatars or nicknames as the basis for judging true gender.

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